Robot path optimization method and system based on improved particle swarm algorithm

Abstract

The invention discloses a robot path optimization method based on an improved particle swarm algorithm. The robot path optimization method comprises the following steps of constructing a gridding mapfor a robot activity space and obstacles in the activity space; dividing the gridded map into an obstacle area and a free area; constructing a path optimization function according to the first path ofthe robot and an optimization target; adopting an improved particle swarm algorithm to iterate an optimal position value in the free area, and calculating an optimal detour path of the robot in the free area according to the optimal position value; wherein the improved particle swarm optimization algorithm specifically comprises the following steps of taking a path optimization function as an appropriate value function of particles; and taking the dynamic inertia weight as the inertia weight of the particle swarm algorithm. The method is advantaged in that path optimization can be carried outin a complex environment, and globality of path optimization is improved. The invention further discloses a robot path optimization system based on the improved particle swarm algorithm, and a storage medium.

Description

technical field [0001] The invention relates to the technical field of path optimization, in particular to a robot path optimization method, system and storage medium based on an improved particle swarm algorithm. Background technique [0002] In the early stage of intelligent construction of substations, robots are mainly introduced to replace manual equipment inspections. During the inspection process, inspection robots need to solve problems such as avoiding obstacles, reaching destination points, and planning routes. Technologies such as lasers, ultrasonic radars, and visual sensors are usually used. Scan the substation environment in advance and then analyze the optimal path. [0003] In the prior art, common robot path optimization methods based on the improved particle swarm optimization algorithm include graph search method, artificial potential field method, and particle swarm optimization algorithm. However, graph search methods and artificial potential field meth...

AI Technical Summary

Problems solved by technology

However, graph search methods and artificial potential field methods can converge quickly in simple environments, but perform poorly in complex environment models

The particle swarm optimization algorithm can generate optimal paths for environments with multiple complex structures, but the disadvantage is that the number of iterations is large, and when the starting point or weight is not well selected, it is easy to fall into a local optimal solution, and the generated path is relatively long

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